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chevron_left Creating DataFrames Cookbook
Combining multiple Series into a DataFrameCombining multiple Series to form a DataFrameConverting a Series to a DataFrameConverting list of lists into DataFrameConverting list to DataFrameConverting percent string into a numeric for read_csvConverting scikit-learn dataset to Pandas DataFrameConverting string data into a DataFrameCreating a DataFrame from a stringCreating a DataFrame using listsCreating a DataFrame with different type for each columnCreating a DataFrame with empty valuesCreating a DataFrame with missing valuesCreating a DataFrame with random numbersCreating a DataFrame with zerosCreating a MultiIndex DataFrameCreating a Pandas DataFrameCreating a single DataFrame from multiple filesCreating empty DataFrame with only column labelsFilling missing values when using read_csvImporting DatasetImporting tables from PostgreSQL as Pandas DataFramesInitialising a DataFrame using a constantInitialising a DataFrame using a dictionaryInitialising a DataFrame using a list of dictionariesInserting lists into a DataFrame cellKeeping leading zeroes when using read_csvParsing dates when using read_csvPreventing strings from getting parsed as NaN for read_csvReading data from GitHubReading file without headerReading large CSV files in chunksReading n random lines using read_csvReading space-delimited filesReading specific columns from fileReading tab-delimited filesReading the first few lines of a file to create DataFrameReading the last n lines of a fileReading URL using read_csvReading zipped csv file as a DataFrameRemoving Unnamed:0 columnResolving ParserError: Error tokenizing dataSaving DataFrame as zipped csvSkipping rows without skipping header for read_csvSpecifying data type for read_csvTreating missing values as empty strings rather than NaN for read_csv
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chevron_left Creating DataFrames Cookbook
Combining multiple Series into a DataFrameCombining multiple Series to form a DataFrameConverting a Series to a DataFrameConverting list of lists into DataFrameConverting list to DataFrameConverting percent string into a numeric for read_csvConverting scikit-learn dataset to Pandas DataFrameConverting string data into a DataFrameCreating a DataFrame from a stringCreating a DataFrame using listsCreating a DataFrame with different type for each columnCreating a DataFrame with empty valuesCreating a DataFrame with missing valuesCreating a DataFrame with random numbersCreating a DataFrame with zerosCreating a MultiIndex DataFrameCreating a Pandas DataFrameCreating a single DataFrame from multiple filesCreating empty DataFrame with only column labelsFilling missing values when using read_csvImporting DatasetImporting tables from PostgreSQL as Pandas DataFramesInitialising a DataFrame using a constantInitialising a DataFrame using a dictionaryInitialising a DataFrame using a list of dictionariesInserting lists into a DataFrame cellKeeping leading zeroes when using read_csvParsing dates when using read_csvPreventing strings from getting parsed as NaN for read_csvReading data from GitHubReading file without headerReading large CSV files in chunksReading n random lines using read_csvReading space-delimited filesReading specific columns from fileReading tab-delimited filesReading the first few lines of a file to create DataFrameReading the last n lines of a fileReading URL using read_csvReading zipped csv file as a DataFrameRemoving Unnamed:0 columnResolving ParserError: Error tokenizing dataSaving DataFrame as zipped csvSkipping rows without skipping header for read_csvSpecifying data type for read_csvTreating missing values as empty strings rather than NaN for read_csv
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Initialising a DataFrame using a dictionary in Pandas

Pandas
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Creating DataFrames Cookbook
schedule Jul 1, 2022
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Using a dictionary of arrays

To create a DataFrame using a dictionary of arrays:

df = pd.DataFrame({"A":[3,4], "B":[5,6]})
df
A B
0 3 5
1 4 6

Here, the key-value pair of the dictionary is as follows:

  • key: column label

  • value: values of that column

Also, since the data does not contain any index (row labels), the default integer indices ([0,1]) are used.

Using a nested dictionary

To create a DataFrame using a nested dictionary:

col_one = {"a":3, "b":4}
col_two = {"a":5, "b":6}
df = pd.DataFrame({"A":col_one, "B":col_two})
df
A B
a 3 5
b 4 6

Here, we've specified the index in col_one and col_two.

Using a dictionary whose key-value pair represents a row

Consider the following dictionary that holds some data:

data = {
"alex": 20,
"bob": 30,
"cathy": 40
}

We want to create a DataFrame whose values are all the key-value pairs of data. Pandas does not provide a direct solution for this, so we ourselves must extract the keys and values of data as lists:

arr_name = list(data.keys())
arr_age = list(data.values())

We can then use the first approach of initialising a DataFrame using a dictionary of arrays:

df = pd.DataFrame({"name":arr_name, "age":arr_age})
df
name age
0 alex 20
1 bob 30
2 cathy 40
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Published by Isshin Inada
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